TY - GEN

T1 - Mean field variational approximation for continuous-time Bayesian networks

AU - Cohn, Ido

AU - El-Hay, Tal

AU - Friedman, Nir

AU - Kupferman, Raz

PY - 2009

Y1 - 2009

N2 - Continuous-time Bayesian networks is a natural structured representation language for multicomponent stochastic processes that evolve continuously over time. Despite the compact representation, inference in such models is intractable even in relatively simple structured networks. Here we introduce a mean field variational approximation in which we use a product of inhomogeneous Markov processes to approximate a distribution over trajectories. This variational approach leads to a globally consistent distribution, which can be efficiently queried. Additionally, it provides a lower bound on the probability of observations, thus making it attractive for learning tasks. We provide the theoretical foundations for the approximation, an efficient implementation that exploits the wide range of highly optimized ordinary differential equations (ODE) solvers, experimentally explore characterizations of processes for which this approximation is suitable, and show applications to a large-scale realworld inference problem.

AB - Continuous-time Bayesian networks is a natural structured representation language for multicomponent stochastic processes that evolve continuously over time. Despite the compact representation, inference in such models is intractable even in relatively simple structured networks. Here we introduce a mean field variational approximation in which we use a product of inhomogeneous Markov processes to approximate a distribution over trajectories. This variational approach leads to a globally consistent distribution, which can be efficiently queried. Additionally, it provides a lower bound on the probability of observations, thus making it attractive for learning tasks. We provide the theoretical foundations for the approximation, an efficient implementation that exploits the wide range of highly optimized ordinary differential equations (ODE) solvers, experimentally explore characterizations of processes for which this approximation is suitable, and show applications to a large-scale realworld inference problem.

UR - http://www.scopus.com/inward/record.url?scp=80053135175&partnerID=8YFLogxK

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AN - SCOPUS:80053135175

T3 - Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, UAI 2009

SP - 91

EP - 100

BT - Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, UAI 2009

PB - AUAI Press

ER -